This paper provides a comprehensive survey of deep domain adaptation (DDA) methods for computer vision applications, addressing the lack of labeled data in new tasks and domains. The authors present a taxonomy of different DDA scenarios based on the properties of data that define how two domains diverge. They categorize DDA approaches into several categories based on training loss and analyze and compare state-of-the-art methods. The survey also covers computer vision applications beyond image classification, such as face recognition, semantic segmentation, and object detection. Additionally, the paper highlights potential deficiencies of current methods and suggests future research directions. The key contributions include a detailed taxonomy of DDA scenarios, an extension of Csurka's work on training loss categories, a review of multi-step DDA methods, and a survey of various computer vision applications. The paper aims to provide a comprehensive understanding of DDA methods and their applications in computer vision.This paper provides a comprehensive survey of deep domain adaptation (DDA) methods for computer vision applications, addressing the lack of labeled data in new tasks and domains. The authors present a taxonomy of different DDA scenarios based on the properties of data that define how two domains diverge. They categorize DDA approaches into several categories based on training loss and analyze and compare state-of-the-art methods. The survey also covers computer vision applications beyond image classification, such as face recognition, semantic segmentation, and object detection. Additionally, the paper highlights potential deficiencies of current methods and suggests future research directions. The key contributions include a detailed taxonomy of DDA scenarios, an extension of Csurka's work on training loss categories, a review of multi-step DDA methods, and a survey of various computer vision applications. The paper aims to provide a comprehensive understanding of DDA methods and their applications in computer vision.